{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Visual Representation\n",
        "\n",
        "<img src=\"../images/reliable_rag.svg\" alt=\"Reliable-RAG\" width=\"300\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Package Installation and Imports\n",
        "\n",
        "The cell below installs all necessary packages required to run this notebook.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Install required packages\n",
        "!pip install langchain langchain-community python-dotenv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "### LLMs\n",
        "import os\n",
        "from dotenv import load_dotenv\n",
        "\n",
        "# Load environment variables from '.env' file\n",
        "load_dotenv()\n",
        "\n",
        "os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY') # For LLM -- llama-3.1-8b (small) & mixtral-8x7b-32768 (large)\n",
        "os.environ['COHERE_API_KEY'] = os.getenv('COHERE_API_KEY') # For embedding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Vectorstore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "### Build Index\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain_cohere import CohereEmbeddings\n",
        "\n",
        "# Set embeddings\n",
        "embedding_model = CohereEmbeddings(model=\"embed-english-v3.0\")\n",
        "\n",
        "# Docs to index\n",
        "urls = [\n",
        "    \"https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/?ref=dl-staging-website.ghost.io\",\n",
        "    \"https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-2-reflection/?ref=dl-staging-website.ghost.io\",\n",
        "    \"https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-3-tool-use/?ref=dl-staging-website.ghost.io\",\n",
        "    \"https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-4-planning/?ref=dl-staging-website.ghost.io\",\n",
        "    \"https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-5-multi-agent-collaboration/?ref=dl-staging-website.ghost.io\"\n",
        "]\n",
        "\n",
        "# Load\n",
        "docs = [WebBaseLoader(url).load() for url in urls]\n",
        "docs_list = [item for sublist in docs for item in sublist]\n",
        "\n",
        "# Split\n",
        "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
        "    chunk_size=500, chunk_overlap=0\n",
        ")\n",
        "doc_splits = text_splitter.split_documents(docs_list)\n",
        "\n",
        "# Add to vectorstore\n",
        "vectorstore = Chroma.from_documents(\n",
        "    documents=doc_splits,\n",
        "    collection_name=\"rag\",\n",
        "    embedding=embedding_model,\n",
        ")\n",
        "\n",
        "retriever = vectorstore.as_retriever(\n",
        "                search_type=\"similarity\",\n",
        "                search_kwargs={'k': 4}, # number of documents to retrieve\n",
        "            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Question"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "question = \"what are the differnt kind of agentic design patterns?\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "docs = retriever.invoke(question)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Check what our doc looklike"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Title: Agentic Design Patterns Part 5, Multi-Agent Collaboration\n",
            "\n",
            "Source: https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-5-multi-agent-collaboration/?ref=dl-staging-website.ghost.io\n",
            "\n",
            "Content: mature patterns of Reflection and Tool Use are more reliable. I hope you enjoy playing with these agentic design patterns and that they produce amazing results for you! If you're interested in learning more, I recommend: ‚ÄúCommunicative Agents for Software Development,‚Äù Qian et al. (2023) (the ChatDev paper)‚ÄúAutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation,‚Äù Wu et al. (2023) ‚ÄúMetaGPT: Meta Programming for a Multi-Agent Collaborative Framework,‚Äù Hong et al. (2023)Keep learning!AndrewRead \"Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance\"Read \"Agentic Design Patterns Part 2: Reflection\" Read \"Agentic Design Patterns Part 3: Tool Use\"Read \"Agentic Design Patterns Part 4: Planning\" ShareSubscribe to The BatchStay updated with weekly AI News and Insights delivered to your inboxCoursesThe BatchCommunityCareersAbout\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"Title: {docs[0].metadata['title']}\\n\\nSource: {docs[0].metadata['source']}\\n\\nContent: {docs[0].page_content}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Check document relevancy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain_groq import ChatGroq\n",
        "\n",
        "# Data model\n",
        "class GradeDocuments(BaseModel):\n",
        "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
        "\n",
        "    binary_score: str = Field(\n",
        "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
        "    )\n",
        "\n",
        "\n",
        "# LLM with function call\n",
        "llm = ChatGroq(model=\"llama-3.1-8b-instant\", temperature=0)\n",
        "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
        "\n",
        "# Prompt\n",
        "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
        "    If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
        "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
        "    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
        "grade_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\"system\", system),\n",
        "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "retrieval_grader = grade_prompt | structured_llm_grader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Filter out the non-relevant docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mature patterns of Reflection and Tool Use are more reliable. I hope you enjoy playing with these agentic design patterns and that they produce amazing results for you! If you're interested in learning more, I recommend: ‚ÄúCommunicative Agents for Software Development,‚Äù Qian et al. (2023) (the ChatDev paper)‚ÄúAutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation,‚Äù Wu et al. (2023) ‚ÄúMetaGPT: Meta Programming for a Multi-Agent Collaborative Framework,‚Äù Hong et al. (2023)Keep learning!AndrewRead \"Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance\"Read \"Agentic Design Patterns Part 2: Reflection\" Read \"Agentic Design Patterns Part 3: Tool Use\"Read \"Agentic Design Patterns Part 4: Planning\" ShareSubscribe to The BatchStay updated with weekly AI News and Insights delivered to your inboxCoursesThe BatchCommunityCareersAbout \n",
            " --------------------------------------------------\n",
            "binary_score='yes' \n",
            "\n",
            "I recommend: ‚ÄúGorilla: Large Language Model Connected with Massive APIs,‚Äù Patil et al. (2023)‚ÄúMM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action,‚Äù Yang et al. (2023)‚ÄúEfficient Tool Use with Chain-of-Abstraction Reasoning,‚Äù Gao et al. (2024)   Both Tool Use and Reflection, which I described in last week‚Äôs letter, are design patterns that I can get to work fairly reliably on my applications ‚Äî both are capabilities well worth learning about. In future letters, I‚Äôll describe the Planning and Multi-agent collaboration design patterns. They allow AI agents to do much more but are less mature, less predictable ‚Äî albeit very exciting ‚Äî technologies. Keep learning!AndrewRead \"Agentic Design Patterns Part 1: Four AI agent strategies that improve GPT-4 and GPT-3.5 performance\"Read \"Agentic Design Patterns Part 2: Reflection\"Read \"Agentic Design Patterns Part 4: Planning\"Read \"Agentic Design Patterns Part 5: Multi-Agent Collaboration\"ShareSubscribe to The BatchStay updated with weekly AI News and Insights delivered to your inboxCoursesThe BatchCommunityCareersAbout \n",
            " --------------------------------------------------\n",
            "binary_score='yes' \n",
            "\n",
            "67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%. Open source agent tools and the academic literature on agents are proliferating, making this an exciting time but also a confusing one. To help put this work into perspective, Iâ€™d like to share a framework for categorizing design patterns for building agents. My team AI Fund is successfully using these patterns in many applications, and I hope you find them useful.Reflection: The LLM examines its own work to come up with ways to improve it. Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.Next week, Iâ€™ll elaborate on these design patterns and offer suggested readings for each.Keep learning!AndrewRead \"Agentic Design Patterns Part 2: Reflection\"Read \"Agentic Design Patterns Part 3, Tool Use\"Read \"Agentic Design Patterns Part 4: Planning\"Read \"Agentic Design Patterns Part 5: Multi-Agent Collaboration\"ShareSubscribe to The BatchStay updated with weekly AI News and Insights delivered to your inboxCoursesThe BatchCommunityCareersAbout \n",
            " --------------------------------------------------\n",
            "binary_score='yes' \n",
            "\n",
            "Agentic Design Patterns Part 4: Planningüåü New Course! Enroll in Large Multimodal Model Prompting with GeminiExplore CoursesAI NewsletterThe BatchAndrew's LetterData PointsML ResearchBlogCommunityForumEventsAmbassadorsAmbassador SpotlightResourcesCompanyAboutCareersContactStart LearningWeekly IssuesAndrew's LettersData PointsML ResearchBusinessScienceAI & SocietyCultureHardwareAI CareersAboutSubscribeThe BatchLettersArticleAgentic Design Patterns Part 4, Planning Large language models can drive powerful agents to execute complex tasks if you ask them to plan the steps before they act.LettersTechnical InsightsPublishedApr 10, 2024Reading time3 min readShareDear friends,Planning is a key agentic AI design pattern in which we use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a larger task. For example, if we ask an agent to do online research on a given topic, we might use an LLM to break down the objective into smaller subtasks, such as researching specific subtopics, synthesizing findings, and compiling a report. Many people had a ‚ÄúChatGPT moment‚Äù shortly after ChatGPT was released, when they played with it and were surprised that it significantly exceeded their expectation of what AI can do. If you have not yet had a similar ‚ÄúAI Agentic moment,‚Äù I hope you will soon. I had one several months ago, when I presented a live demo of a research agent I had implemented that had access to various online search tools. I had tested this agent multiple times privately, during which it consistently used a web search tool to gather information and wrote up a summary. During the live demo, though, the web search API unexpectedly returned with a rate limiting error. I thought my demo was about to fail publicly, and I dreaded what was to come next. To my surprise, the agent pivoted deftly to a Wikipedia search tool ‚Äî which I had forgotten I‚Äôd given it ‚Äî and completed the task using Wikipedia instead of web search. This was an AI Agentic moment of surprise for me. I think many people who haven‚Äôt experienced such a moment yet will do so in the coming months. It‚Äôs \n",
            " --------------------------------------------------\n",
            "binary_score='yes' \n",
            "\n"
          ]
        }
      ],
      "source": [
        "docs_to_use = []\n",
        "for doc in docs:\n",
        "    print(doc.page_content, '\\n', '-'*50)\n",
        "    res = retrieval_grader.invoke({\"question\": question, \"document\": doc.page_content})\n",
        "    print(res,'\\n')\n",
        "    if res.binary_score == 'yes':\n",
        "        docs_to_use.append(doc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Generate Result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "According to the retrieved documents, there are four main agentic design patterns:\n",
            "\n",
            "1. **Reflection**: The LLM examines its own work to come up with ways to improve it.\n",
            "2. **Tool Use**: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.\n",
            "3. **Planning**: The LLM comes up with, and executes, a multistep plan to achieve a goal.\n",
            "4. **Multi-agent collaboration**: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.\n"
          ]
        }
      ],
      "source": [
        "from langchain_core.output_parsers import StrOutputParser\n",
        "\n",
        "# Prompt\n",
        "system = \"\"\"You are an assistant for question-answering tasks. Answer the question based upon your knowledge. \n",
        "Use three-to-five sentences maximum and keep the answer concise.\"\"\"\n",
        "prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\"system\", system),\n",
        "        (\"human\", \"Retrieved documents: \\n\\n <docs>{documents}</docs> \\n\\n User question: <question>{question}</question>\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "# LLM\n",
        "llm = ChatGroq(model=\"llama-3.1-8b-instant\", temperature=0)\n",
        "\n",
        "# Post-processing\n",
        "def format_docs(docs):\n",
        "    return \"\\n\".join(f\"<doc{i+1}>:\\nTitle:{doc.metadata['title']}\\nSource:{doc.metadata['source']}\\nContent:{doc.page_content}\\n</doc{i+1}>\\n\" for i, doc in enumerate(docs))\n",
        "\n",
        "# Chain\n",
        "rag_chain = prompt | llm | StrOutputParser()\n",
        "\n",
        "# Run\n",
        "generation = rag_chain.invoke({\"documents\":format_docs(docs_to_use), \"question\": question})\n",
        "print(generation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Check for Hallucinations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "binary_score='yes'\n"
          ]
        }
      ],
      "source": [
        "# Data model\n",
        "class GradeHallucinations(BaseModel):\n",
        "    \"\"\"Binary score for hallucination present in 'generation' answer.\"\"\"\n",
        "\n",
        "    binary_score: str = Field(\n",
        "        ...,\n",
        "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
        "    )\n",
        "\n",
        "# LLM with function call\n",
        "llm = ChatGroq(model=\"llama-3.1-8b-instant\", temperature=0)\n",
        "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
        "\n",
        "# Prompt\n",
        "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
        "    Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
        "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
        "    [\n",
        "        (\"system\", system),\n",
        "        (\"human\", \"Set of facts: \\n\\n <facts>{documents}</facts> \\n\\n LLM generation: <generation>{generation}</generation>\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
        "\n",
        "response = hallucination_grader.invoke({\"documents\": format_docs(docs_to_use), \"generation\": generation})\n",
        "print(response)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Highlight used docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "from typing import List\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "# Data model\n",
        "class HighlightDocuments(BaseModel):\n",
        "    \"\"\"Return the specific part of a document used for answering the question.\"\"\"\n",
        "\n",
        "    id: List[str] = Field(\n",
        "        ...,\n",
        "        description=\"List of id of docs used to answers the question\"\n",
        "    )\n",
        "\n",
        "    title: List[str] = Field(\n",
        "        ...,\n",
        "        description=\"List of titles used to answers the question\"\n",
        "    )\n",
        "\n",
        "    source: List[str] = Field(\n",
        "        ...,\n",
        "        description=\"List of sources used to answers the question\"\n",
        "    )\n",
        "\n",
        "    segment: List[str] = Field(\n",
        "        ...,\n",
        "        description=\"List of direct segements from used documents that answers the question\"\n",
        "    )\n",
        "\n",
        "# LLM\n",
        "llm = ChatGroq(model=\"mixtral-8x7b-32768\", temperature=0)\n",
        "\n",
        "# parser\n",
        "parser = PydanticOutputParser(pydantic_object=HighlightDocuments)\n",
        "\n",
        "# Prompt\n",
        "system = \"\"\"You are an advanced assistant for document search and retrieval. You are provided with the following:\n",
        "1. A question.\n",
        "2. A generated answer based on the question.\n",
        "3. A set of documents that were referenced in generating the answer.\n",
        "\n",
        "Your task is to identify and extract the exact inline segments from the provided documents that directly correspond to the content used to \n",
        "generate the given answer. The extracted segments must be verbatim snippets from the documents, ensuring a word-for-word match with the text \n",
        "in the provided documents.\n",
        "\n",
        "Ensure that:\n",
        "- (Important) Each segment is an exact match to a part of the document and is fully contained within the document text.\n",
        "- The relevance of each segment to the generated answer is clear and directly supports the answer provided.\n",
        "- (Important) If you didn't used the specific document don't mention it.\n",
        "\n",
        "Used documents: <docs>{documents}</docs> \\n\\n User question: <question>{question}</question> \\n\\n Generated answer: <answer>{generation}</answer>\n",
        "\n",
        "<format_instruction>\n",
        "{format_instructions}\n",
        "</format_instruction>\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    template= system,\n",
        "    input_variables=[\"documents\", \"question\", \"generation\"],\n",
        "    partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
        ")\n",
        "\n",
        "# Chain\n",
        "doc_lookup = prompt | llm | parser\n",
        "\n",
        "# Run\n",
        "lookup_response = doc_lookup.invoke({\"documents\":format_docs(docs_to_use), \"question\": question, \"generation\": generation})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ID: doc3\n",
            "Title: Four AI Agent Strategies That Improve GPT-4 and GPT-3.5 Performance\n",
            "Source: https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/?ref=dl-staging-website.ghost.io\n",
            "Text Segment: Reflection: The LLM examines its own work to come up with ways to improve it.\\nTool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.\\nPlanning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).\\nMulti-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for id, title, source, segment in zip(lookup_response.id, lookup_response.title, lookup_response.source, lookup_response.segment):\n",
        "    print(f\"ID: {id}\\nTitle: {title}\\nSource: {source}\\nText Segment: {segment}\\n\")"
      ]
    },
    {
      "attachments": {
        "image.png": {
          "image/png": 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        }
      },
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Text segment in the source\n",
        "\n",
        "![image.png](attachment:image.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=all-rag-techniques--reliable-rag)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}